Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2025-12-18 Meeting participants: JanieceGarcia, read.ai meeting notes, Yvette’s Notetaker (Otter.ai), Samuel Roberts, MattBurns, Uttam Kumaran, YvetteRuiz
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1 00:01:45.010 ⇒ 00:01:46.060 Samuel Roberts: Hello.
2 00:02:18.340 ⇒ 00:02:20.200 JanieceGarcia: Hi, sorry about that, I was…
3 00:02:20.710 ⇒ 00:02:21.150 Samuel Roberts: Oh, you’re funny.
4 00:02:21.970 ⇒ 00:02:23.109 Samuel Roberts: Not a problem.
5 00:02:23.110 ⇒ 00:02:24.980 MattBurns: No worries, how you guys doing?
6 00:02:25.480 ⇒ 00:02:26.899 JanieceGarcia: Good, how are you, Matt?
7 00:02:27.150 ⇒ 00:02:28.190 MattBurns: Good, good.
8 00:02:28.940 ⇒ 00:02:31.520 JanieceGarcia: I’m surprised I haven’t seen you today.
9 00:02:32.350 ⇒ 00:02:36.320 MattBurns: I’ve been in meetings most of the day, as usual, so…
10 00:02:38.300 ⇒ 00:02:41.850 MattBurns: Hello. Hi, Mom! Hey, how are you?
11 00:02:42.870 ⇒ 00:02:44.639 MattBurns: Good, good. How are you?
12 00:02:44.950 ⇒ 00:02:45.750 Uttam Kumaran: Good.
13 00:02:46.990 ⇒ 00:02:50.070 MattBurns: Excited for it. I did text the fish,
14 00:02:50.680 ⇒ 00:02:54.289 MattBurns: he’s still in India, but he’s getting back, I think, today.
15 00:02:54.880 ⇒ 00:02:55.410 MattBurns: So…
16 00:02:55.410 ⇒ 00:02:55.770 Uttam Kumaran: Okay.
17 00:02:56.110 ⇒ 00:02:58.670 MattBurns: He’d either get with you, he…
18 00:02:58.840 ⇒ 00:03:03.800 MattBurns: I heard from him on Tuesday, and he said either late this week or early next week, so…
19 00:03:03.800 ⇒ 00:03:12.580 Uttam Kumaran: Okay, okay. I think Bo also said he was gonna message… there’s another administrator, so… I think I… it’ll…
20 00:03:12.960 ⇒ 00:03:22.789 Uttam Kumaran: Worst case, no problem. We got Evolve data, I just… I’m getting some access from Les, and so I feel pretty good.
21 00:03:22.790 ⇒ 00:03:23.480 MattBurns: Okay.
22 00:03:24.250 ⇒ 00:03:27.790 MattBurns: Again, the only thing we’re doing with
23 00:03:28.060 ⇒ 00:03:34.390 MattBurns: Nitesh currently is on the dream, on the… on the sales piece, not on the full app. So, anyway.
24 00:03:34.390 ⇒ 00:03:38.009 Uttam Kumaran: Okay, yeah, that’s all I’m looking for, and really, that’s just gonna help me look at
25 00:03:38.130 ⇒ 00:03:47.970 Uttam Kumaran: like, how we’re logging deals, like, sales velocity, and then also, like, that’ll be the… that’s really gonna be a lot of the purview into the… some of the commercial stuff, so…
26 00:03:47.970 ⇒ 00:03:49.570 MattBurns: Got it. Okay.
27 00:03:51.580 ⇒ 00:03:54.970 MattBurns: You and, I think you and Bo and Steven are meeting…
28 00:03:55.820 ⇒ 00:04:02.179 Uttam Kumaran: We actually met yesterday. Oh, and then we will be meeting, after the… after the break in January.
29 00:04:02.180 ⇒ 00:04:04.030 MattBurns: Gotcha. So, yeah.
30 00:04:05.600 ⇒ 00:04:07.070 JanieceGarcia: Okay, let me…
31 00:04:07.190 ⇒ 00:04:17.470 JanieceGarcia: Yes, and Yvette’s running a little bit late, Utam, but she will be in as soon as she’s done with her meeting that she’s in right now, and Steven will not be in this meeting today.
32 00:04:17.860 ⇒ 00:04:27.070 Uttam Kumaran: Okay, cool. I… I think it’ll be… we could just begin, and I think it’s… I have a couple things to cover. So…
33 00:04:27.360 ⇒ 00:04:29.150 Uttam Kumaran: Let me get this going.
34 00:04:30.220 ⇒ 00:04:34.899 JanieceGarcia: While you’re doing that, I will let you know I did update everyone’s emails, so now they are all…
35 00:04:34.900 ⇒ 00:04:40.160 Uttam Kumaran: Oh, great, okay, so that was an item for me to… poke at again today, so…
36 00:04:41.710 ⇒ 00:04:42.920 JanieceGarcia: It’s done!
37 00:04:43.160 ⇒ 00:04:44.070 Uttam Kumaran: Okay, great.
38 00:04:44.990 ⇒ 00:04:56.590 Uttam Kumaran: then we will reload. Some of the stuff may not be perfectly up-to-date then, but I’ll reload, I can send this out. So, yeah, so, I… I feel like we’re…
39 00:04:57.400 ⇒ 00:05:13.259 Uttam Kumaran: we’re still, again, as soon as we get the rest of the folks, we’re gonna go in and show the total exchanges. I’m also just gonna, after I kind of walk through the deck today, just also share a couple of changes we made to the dashboard. You know, my hope is that this is something
40 00:05:13.260 ⇒ 00:05:21.649 Uttam Kumaran: you know, that you don’t have to wait for, you know, us every week to kind of show what the highlights are. I think what you do want us to do is show, like, what
41 00:05:21.650 ⇒ 00:05:30.149 Uttam Kumaran: what, like, what the key things are and, like, how we can change, but I want to make it really open for people to say, like, what departments are… are changing, and so…
42 00:05:30.210 ⇒ 00:05:32.950 Uttam Kumaran: you know, I… I spent some time with David.
43 00:05:33.180 ⇒ 00:05:39.670 Uttam Kumaran: Last week, and so even for him and his team, I’m getting them into here, and I actually told Amber that
44 00:05:40.020 ⇒ 00:05:46.480 Uttam Kumaran: I want to try to get more of the trainers into here so more people can start to see their data.
45 00:05:46.620 ⇒ 00:05:50.129 Uttam Kumaran: But yeah, I feel like we’re… we’re… we’re doing well.
46 00:05:50.160 ⇒ 00:06:05.439 Uttam Kumaran: on usage, and, you know, we are, like, 20% up since, the last 7 days. The other thing that was, you know, big this week, I think, was just all the fixes on the zip code side. So, this is something that, like, I think we just had a few weeks of hiccups with, and I sort of…
47 00:06:05.440 ⇒ 00:06:10.610 Uttam Kumaran: was like, hey guys, we have to sort of remediate this. And so we’ve,
48 00:06:10.610 ⇒ 00:06:28.439 Uttam Kumaran: we’ve changed a lot, and I think, you know, maybe, Sam, I can even give you a couple minutes to talk about some of the changes today, and some of the changes that we’ll kind of… we’re driving towards before the holidays. But really, the biggest thing is the zip code database
49 00:06:28.440 ⇒ 00:06:39.720 Uttam Kumaran: you know, as you guys know, it’s very tricky. It’s, lookups of, people, of places, of, like, types of services, and…
50 00:06:39.850 ⇒ 00:06:46.439 Uttam Kumaran: It is… when you have a spreadsheet, it’s easy for a person to go through and understand,
51 00:06:47.020 ⇒ 00:07:09.300 Uttam Kumaran: like, mosquito systems versus, like, mosquito service, but when you’re typing to an AI bot, you may just be short with it, and then it has to figure out. And so I think when we first built this, we were a little bit more generous with assuming that our inputs were going to be very, very clear, and one of the things that I think was clear with the team
52 00:07:09.300 ⇒ 00:07:15.449 Uttam Kumaran: is that we have to be a little bit more smart in a couple of different ways. So one is, like.
53 00:07:15.500 ⇒ 00:07:24.040 Uttam Kumaran: if someone is asking a question, and we don’t… it’s not in the database, there’s one or two things that are happening. Either A,
54 00:07:24.040 ⇒ 00:07:37.769 Uttam Kumaran: It doesn’t exist, and so, okay, that is either a fact that it’s missing, and so ABC team has to go in and add that in, or that person assumed it was there, it’s not there, and there’s, like.
55 00:07:37.990 ⇒ 00:07:52.230 Uttam Kumaran: okay, there’s not… there’s no action, right? The third thing is that they asked it, and then we just ran the wrong query, and that’s on us. For the user, though, all they’re getting back is, hey, it’s not there, and…
56 00:07:52.230 ⇒ 00:08:11.550 Uttam Kumaran: ask your supervisor, right? And so, for me, I went to the team and said, that’s not enough. We need to do a little bit more in explaining to the user what the thing we ran is, and what we found, and the potential things that could go wrong. You know, and so, for us, we’re moving that in a couple ways. One, is
57 00:08:12.260 ⇒ 00:08:28.379 Uttam Kumaran: this entire, sort of, lookup system we moved, we, like, upgraded. So you should see that it’s a bit faster, and also, it’s now a lot more flexible for us to fix quicker. The second piece is, I’ve pushed the team to start to
58 00:08:28.380 ⇒ 00:08:39.400 Uttam Kumaran: actually attach error codes based on what the issue… what the feedback is. For example, if we get feedback, or we see that the, that Andy didn’t return everything.
59 00:08:39.520 ⇒ 00:08:50.310 Uttam Kumaran: we’re actually gonna build another step that goes and finds out why it didn’t find anything, and what the potential, like, reasons are. Because right now, we see that there’s just, like.
60 00:08:50.590 ⇒ 00:09:05.929 Uttam Kumaran: tons of zip code-related issues coming, but of course, we find that there’s some stuff that’s on our team to fix, there’s sometimes that just doesn’t… that’s not there. And so the faster that we can triage that, is, like, what we’re driving towards, and I think that’s something that,
61 00:09:06.260 ⇒ 00:09:10.449 Uttam Kumaran: we’re doing. I don’t know, Sam, did I… did I miss anything, or did you want to, like, highlight?
62 00:09:10.990 ⇒ 00:09:12.200 Uttam Kumaran: Anything there?
63 00:09:14.500 ⇒ 00:09:19.320 Samuel Roberts: Sorry, no, I think you pretty much covered it. I think the… being clear with the…
64 00:09:19.510 ⇒ 00:09:27.930 Samuel Roberts: when there’s an error of some sort, or no data is returned for whatever reason, I think it’s going to be a big thing, because we can show what… what was searched, and that will help us
65 00:09:28.290 ⇒ 00:09:43.820 Samuel Roberts: know, okay, it messed up the query, or oh, there’s a slightly different way that we talk about it than is in the database, or something like that, then that’ll help us, as we build this other agent, make those fixes. And then, I think the other side of it is the feedback that they
66 00:09:43.820 ⇒ 00:09:54.369 Samuel Roberts: the agents do… or the CSRs do provide is nice, because then maybe we know, okay, there is supposed to be someone there. That’s a good signal that, something is missing in the database, and it wasn’t just a…
67 00:09:54.370 ⇒ 00:10:04.299 Samuel Roberts: a lookup problem, or, you know, it’s not returning anything, but there should be a bunch of people. Like, there’s a lot of things we can do from that, but yeah, you pretty much covered the different types of everything.
68 00:10:04.300 ⇒ 00:10:08.910 MattBurns: Well, thanks for explaining that. I used to… Yvette and I talked about this a little bit this morning, and I was
69 00:10:09.120 ⇒ 00:10:13.210 MattBurns: A little perplexed as to why… Andy couldn’t…
70 00:10:13.360 ⇒ 00:10:20.790 MattBurns: give an automatic answer, but your… your explanation, yeah, was telling to me, because I don’t use it, obviously.
71 00:10:20.790 ⇒ 00:10:21.120 Samuel Roberts: Hmm.
72 00:10:21.120 ⇒ 00:10:26.819 MattBurns: On a regular basis, but that makes sense, but… Just a related question, who…
73 00:10:27.240 ⇒ 00:10:32.910 MattBurns: And how many people, and Janiece, maybe you know, who can maintain this? Who can update it? Who can…
74 00:10:33.070 ⇒ 00:10:40.899 MattBurns: Because you know as well as I do, it can change pretty quickly. Who’s updating this database here?
75 00:10:41.160 ⇒ 00:10:54.890 JanieceGarcia: So, I am actually updating that, Matt, and that’s where I was gonna ask. So, once it’s all input into the database, Utam, to make sure, okay, we’re good.
76 00:10:55.010 ⇒ 00:11:05.889 JanieceGarcia: Do I need to go back in and update all of my updates that I’ve done before, or are we kind of starting fresh and clean? What’s on the inspector sheet? What’s on the service areas by branch?
77 00:11:06.190 ⇒ 00:11:16.709 JanieceGarcia: the text sheets, all of those is kind of where we’re starting, because I know no updates have been sent to me since then, but I want to make sure that we have those.
78 00:11:16.910 ⇒ 00:11:19.869 JanieceGarcia: And then… So that’s the first question.
79 00:11:20.360 ⇒ 00:11:32.060 Uttam Kumaran: Yeah, so maybe to say it back, is the question, like, as they’re making changes, they get immediately applied. And so, I guess I’m… maybe if you can say it again. Again? Yeah.
80 00:11:32.060 ⇒ 00:11:41.029 JanieceGarcia: So, because we had, whenever all of this was coming to surface, that’s when I was doing updates in the database.
81 00:11:41.030 ⇒ 00:11:41.690 Uttam Kumaran: Sure.
82 00:11:42.140 ⇒ 00:11:46.660 JanieceGarcia: So, any of those updates, are y’all making those?
83 00:11:46.840 ⇒ 00:11:54.869 JanieceGarcia: Or am I gonna need… when you say, hey, it’s good to go, do I need to go back and redo the updates that I have done before all of this…
84 00:11:55.000 ⇒ 00:11:55.960 JanieceGarcia: brokenness.
85 00:11:56.820 ⇒ 00:12:02.570 Uttam Kumaran: Oh, no, no, no, none of the updates that you’ve sent to the database has…
86 00:12:03.070 ⇒ 00:12:18.070 Uttam Kumaran: Has changed. Has changed, yeah. So, those are all fine. The updates we made, are basically on the things between the person and the database, which is the AI agent. So that’s where we’ve been working, yeah.
87 00:12:18.070 ⇒ 00:12:34.309 Uttam Kumaran: So you won’t have to do those, and I think as an example, like, and again, when we… we saw this… this is, like, the one we’ve been staring at all week, which is, I need to schedule a mosquito misting system in Corpus, right? And I think it’s helpful for us to, like, look at this.
88 00:12:34.540 ⇒ 00:12:48.040 Uttam Kumaran: question in detail, because this is a weird one, because you have mosquito misting, and you have mosquito systems. You don’t have mosquito misting system. And so…
89 00:12:48.040 ⇒ 00:12:48.370 JanieceGarcia: Cotta.
90 00:12:48.370 ⇒ 00:12:59.410 Uttam Kumaran: when Andy looks for, mosquito misting, like, there’s… so this is just the thing that happens. And so before, what we’re… yeah, go ahead, Sam.
91 00:12:59.640 ⇒ 00:13:01.690 Samuel Roberts: No, no, no, no, I was… no, you’re good. Keep going.
92 00:13:01.690 ⇒ 00:13:05.560 Uttam Kumaran: So before, we were… well, we just reply, I’m sorry, I can’t find that.
93 00:13:05.560 ⇒ 00:13:07.620 Samuel Roberts: Right? And so, for me, one, I was like.
94 00:13:07.620 ⇒ 00:13:16.850 Uttam Kumaran: Okay, we do know why it’s not there. A better thing is we should tell the user, hey, I looked for mosquito system
95 00:13:16.960 ⇒ 00:13:30.439 Uttam Kumaran: estimates, right? Like, I look for mosquitoes… I look for mosquito misting, I couldn’t find anything, but I could go look for mosquito system estimates, because there’s stuff for that. Is that what you meant? Right? Like, that’s where this needs to go, right?
96 00:13:30.440 ⇒ 00:13:33.379 MattBurns: And that’s where we can probably help.
97 00:13:33.990 ⇒ 00:13:35.379 MattBurns: Tom, because…
98 00:13:35.500 ⇒ 00:13:50.409 MattBurns: we actually do have a mosquito system, and it’s a misting system. It is a mosquito misting system, but we also have mosquito… but we also have mosquito fogging, and I think that’s probably, Janiece, where we need to
99 00:13:50.650 ⇒ 00:13:53.819 MattBurns: differentiate with the CSRs, it’s like.
100 00:13:53.990 ⇒ 00:13:56.230 MattBurns: No, we have a misting system.
101 00:13:56.920 ⇒ 00:14:00.220 MattBurns: It’s a mosquito misting system. That’s the same thing.
102 00:14:00.570 ⇒ 00:14:10.650 MattBurns: But then we have a service that’s mosquito fogging, and I think that’s where maybe the CSRs are going, well, we have mosquito misting. Well, not really, it’s mosquito fogging.
103 00:14:11.010 ⇒ 00:14:16.099 MattBurns: And then we have a system that does automatically do it, so it’s really…
104 00:14:16.870 ⇒ 00:14:18.609 MattBurns: We need to clarify that as a.
105 00:14:18.610 ⇒ 00:14:19.420 Uttam Kumaran: Yeah.
106 00:14:19.810 ⇒ 00:14:23.369 MattBurns: Because that’s a… that should be a fairly easy fix.
107 00:14:23.480 ⇒ 00:14:27.350 MattBurns: To let everybody know, hey, when you’re talking about the monthly
108 00:14:27.590 ⇒ 00:14:30.940 MattBurns: Fogging program. You need to say fogging, not misting.
109 00:14:31.420 ⇒ 00:14:32.000 JanieceGarcia: Right.
110 00:14:32.000 ⇒ 00:14:33.660 MattBurns: And when you’re talking about
111 00:14:34.200 ⇒ 00:14:44.450 MattBurns: you know, a $5,000 system, that’s an automatic misting system. So those are the differences, but we… in other words, you’re right, though, if
112 00:14:44.720 ⇒ 00:14:51.020 MattBurns: if the feedback you’re getting is, here’s where we’re having the problem, Janiece, that’s where we’ve got to get with
113 00:14:51.200 ⇒ 00:14:59.499 MattBurns: Whether it’s even the service side, you might have to get with Alan or get with whoever’s the expert, and say, well, tell me exactly how this should be phrased.
114 00:14:59.660 ⇒ 00:15:04.539 Uttam Kumaran: And this is also where, like, for the… I want the AI to tell you… here’s what I did look up, though, right?
115 00:15:04.540 ⇒ 00:15:06.500 MattBurns: this, for me, I was like.
116 00:15:06.500 ⇒ 00:15:11.340 Uttam Kumaran: This isn’t good… this doesn’t give the confidence to the user that they… they actually did ask for.
117 00:15:11.340 ⇒ 00:15:12.000 MattBurns: Right.
118 00:15:12.000 ⇒ 00:15:14.640 Uttam Kumaran: Something that… we just said, I’m sorry, we can’t find it.
119 00:15:14.690 ⇒ 00:15:18.890 Uttam Kumaran: Instead, we should say, I looked for this, we can’t find it.
120 00:15:18.890 ⇒ 00:15:35.390 Uttam Kumaran: I did see this, is this what you meant? Right, yeah. And so this is just the, like, on our side, that’s the ergonomic change that we’re gonna make. And so, if you look at a lot of these examples here, we always reply with this sort of, like, I can’t find people.
121 00:15:35.660 ⇒ 00:15:43.310 Uttam Kumaran: But, like, for example, this is, like, do we charge a service fee? It says, I don’t have access to information, but… but then…
122 00:15:43.630 ⇒ 00:15:57.730 Uttam Kumaran: it’s… basically, what we found is, like, there is no service fee for the zip code, but that means that’s because there was no record, I think, right? And so instead, we sh… there’s just certain, like, differences for each type of question that we just need to get a little bit
123 00:15:58.230 ⇒ 00:15:58.810 Uttam Kumaran: Oh, great.
124 00:15:58.810 ⇒ 00:15:59.710 JanieceGarcia: granular.
125 00:15:59.710 ⇒ 00:16:17.660 Uttam Kumaran: Yeah, more… more granular. And then there is some stuff that is, like, just… just wrong, that there’s a reason for. For example, this is, like, for a holiday… who is… who can I schedule a holiday light estimate? It said this is Jose, but instead, it… it pulled a technician instead of an inspector.
126 00:16:18.130 ⇒ 00:16:27.280 Uttam Kumaran: Okay, that’s fine. But for example, if we look at this one, this says, who is the aeration tech for Austin? It says there’s nobody… there’s nobody here.
127 00:16:27.590 ⇒ 00:16:33.359 Uttam Kumaran: And that’s true, there was nobody assigned, so I feel like, okay, what… yeah, I mean, I don’t know.
128 00:16:33.360 ⇒ 00:16:36.200 JanieceGarcia: That would be on us to go in there and update that.
129 00:16:36.200 ⇒ 00:16:44.369 Uttam Kumaran: Yeah, yeah. And so what… really what I’m trying to do here is also indicate, like, who… who owns the mitigation.
130 00:16:44.370 ⇒ 00:16:46.390 Samuel Roberts: Oh, yeah, I think it’s filtered right now, but yeah.
131 00:16:46.760 ⇒ 00:16:50.729 Uttam Kumaran: Yeah, so right now we’re looking at… so, for example, if we… if we add this…
132 00:16:50.890 ⇒ 00:16:55.709 Uttam Kumaran: There’s gonna be some things that our team is gonna be like, okay, this is an issue, like, there’s some…
133 00:16:55.910 ⇒ 00:17:01.720 Uttam Kumaran: syntax error, perfect. But if there’s some things where it’s like, this person wasn’t found.
134 00:17:01.950 ⇒ 00:17:10.979 Uttam Kumaran: Right? Like, for example, I’m sorry, there’s nobody here. Oh, the feedback… this is… so this is the feedback that we get from the user.
135 00:17:11.270 ⇒ 00:17:11.640 Samuel Roberts: Right.
136 00:17:11.640 ⇒ 00:17:16.900 Uttam Kumaran: Right? So, as soon as we say no, the person says, actually, this should be Michael and Les.
137 00:17:17.569 ⇒ 00:17:25.820 Uttam Kumaran: Okay, cool. So now that gets to get added into the database. Where I told our team to dream is I said.
138 00:17:26.050 ⇒ 00:17:29.430 Uttam Kumaran: We gotta build a system that takes this and sort of gives
139 00:17:29.600 ⇒ 00:17:40.590 Uttam Kumaran: Janice, like, a one-click thing to be like, is that right? Okay, let’s just go ahead and do that. And so that’s where I think we’re gonna get the system to go, but, like, this is the… this just helps us understand
140 00:17:41.060 ⇒ 00:17:46.960 Uttam Kumaran: okay, some of these are just, like, it’s actually not there. The user may or may not be right.
141 00:17:47.300 ⇒ 00:17:51.889 Uttam Kumaran: like, I kinda do need some guidance. And then there’s gonna be some stuff on our side, which is, like.
142 00:17:52.750 ⇒ 00:17:57.240 Uttam Kumaran: for example, Do we do trash bin cleaning? It doesn’t look like we do.
143 00:17:57.860 ⇒ 00:17:59.929 Uttam Kumaran: Thumbs down. Okay, but…
144 00:18:00.240 ⇒ 00:18:06.619 Uttam Kumaran: it looks like we don’t do it there. And so, there also gotta be things where we’re like.
145 00:18:06.890 ⇒ 00:18:08.630 Uttam Kumaran: I don’t know, we didn’t get any…
146 00:18:09.020 ⇒ 00:18:11.069 Uttam Kumaran: We didn’t get any feedback. Is he, like…
147 00:18:11.510 ⇒ 00:18:20.909 Uttam Kumaran: Is Nathan, like, I wish we did it… I wish we did it there, or is he… is he, like, that’s wrong? And from our side, I don’t… I don’t know, you know?
148 00:18:20.910 ⇒ 00:18:21.710 JanieceGarcia: Yeah.
149 00:18:21.710 ⇒ 00:18:22.110 MattBurns: Yep.
150 00:18:22.110 ⇒ 00:18:22.520 Uttam Kumaran: Yeah.
151 00:18:22.810 ⇒ 00:18:26.580 MattBurns: And that’s one that’s changed recently quite a bit.
152 00:18:26.580 ⇒ 00:18:27.120 Uttam Kumaran: Okay.
153 00:18:27.120 ⇒ 00:18:27.760 MattBurns: You’re right, Joe.
154 00:18:27.760 ⇒ 00:18:28.370 JanieceGarcia: repent.
155 00:18:28.370 ⇒ 00:18:30.630 MattBurns: That’s one we need to update, yeah.
156 00:18:30.860 ⇒ 00:18:31.900 Uttam Kumaran: Yeah. That’s…
157 00:18:31.900 ⇒ 00:18:50.139 JanieceGarcia: And that’s where I’m wanting to know… so when I go through, because I’m doing the triaging and looking at the triage and assigning them, and I’m trying, as they’re triage, I’m trying to see, okay, well, this is actually, hey, I know this is in the service areas by zips, so if it is.
158 00:18:50.140 ⇒ 00:19:00.189 JanieceGarcia: then I’m screenshotting it and already sending it to Casey or Amber, to go ahead, hey, this is in there, what’s happening? And that’s kind of where this has started.
159 00:19:00.430 ⇒ 00:19:16.749 JanieceGarcia: Okay. Because bringing everybody else in is like, oh my goodness, it’s still not, you know, bringing those. And that’s why we brought you into it, Uten, was because of that. So I’m just trying to figure out, as well, if I go through and I triage these.
160 00:19:17.780 ⇒ 00:19:20.650 JanieceGarcia: Do you want me to still do screenshots?
161 00:19:20.780 ⇒ 00:19:23.120 JanieceGarcia: if I do find out that they’re in there.
162 00:19:23.280 ⇒ 00:19:29.410 JanieceGarcia: I guess I want to know where do I need to start and begin. Yeah, I don’t want to up with tickets.
163 00:19:29.410 ⇒ 00:19:37.879 Uttam Kumaran: Yeah, so… so one is, like, we went through and we basically mapped out, like, what are the potential errors, right? And we’re basically built, like, error codes. Okay, there’s, like.
164 00:19:38.150 ⇒ 00:19:50.630 Uttam Kumaran: 15 potential ways this could go wrong, and depending on the way it went wrong, who owns the mitigation, right? Gotcha. And so some of these are like, okay, there’s no text assigned.
165 00:19:51.490 ⇒ 00:19:54.550 Uttam Kumaran: That’s up to Janiece to tell us whether that’s okay or not, right?
166 00:19:54.550 ⇒ 00:19:55.050 JanieceGarcia: Yep.
167 00:19:55.050 ⇒ 00:20:05.240 Uttam Kumaran: But there’s some things where it’s like, hey, it’s outdated, and that’s where you would send one. What I’m trying to… I think that’s Sam for us to figure out, is whether…
168 00:20:05.890 ⇒ 00:20:11.460 Uttam Kumaran: the AI system can infer these, or whether you need Janiece to, like.
169 00:20:11.600 ⇒ 00:20:14.629 Uttam Kumaran: basically tagged this. I don’t… I think…
170 00:20:14.860 ⇒ 00:20:32.859 Uttam Kumaran: instead of a… instead of a screenshot, a better thing is for you to just label that it’s one of these would be better, and probably faster, and we can make these labels available in linear, so you could just tag this as, like, oh, this is there, this is a missing, oh, this is…
171 00:20:33.250 ⇒ 00:20:34.220 Uttam Kumaran: Something else.
172 00:20:34.220 ⇒ 00:20:34.580 JanieceGarcia: 9.
173 00:20:34.580 ⇒ 00:20:35.820 Uttam Kumaran: Yeah. Okay. Yeah.
174 00:20:35.820 ⇒ 00:20:45.309 JanieceGarcia: I’m all up for that. So, that’s gonna bring me to my next question, and Lanier, you were talking about how the tickets are gonna come through and the errors are gonna be there. So, is it going to be.
175 00:20:45.310 ⇒ 00:20:48.529 Uttam Kumaran: Well, we will try. We will try to…
176 00:20:48.530 ⇒ 00:20:53.029 JanieceGarcia: Like, we’re gonna have AI try to give you as much information as you need.
177 00:20:53.030 ⇒ 00:21:02.299 Uttam Kumaran: to do this faster, like, figure out what it is. For example, the AI should be able to say, okay, the user didn’t get anything, so let me just, like.
178 00:21:02.460 ⇒ 00:21:22.280 Uttam Kumaran: But, like, I ran it, maybe it looks like, based on what they asked and what I ran, they just asked for the wrong thing, right? That I want… I want all of those… that context to be given, so that when I… when you look at it and you triage, you’re like, yeah, okay, it’s… it’s right, it is… it is missing, or, okay, actually, it…
179 00:21:22.300 ⇒ 00:21:25.689 Uttam Kumaran: that’s… that’s wrong. It’s like, the data just isn’t there.
180 00:21:25.690 ⇒ 00:21:26.360 JanieceGarcia: Okay.
181 00:21:26.360 ⇒ 00:21:30.780 Uttam Kumaran: You know, so that way you’re not just dealing with thumbs down, and I have to go figure it out.
182 00:21:30.780 ⇒ 00:21:32.639 JanieceGarcia: Read through all of those, and… okay.
183 00:21:32.640 ⇒ 00:21:37.769 Uttam Kumaran: Yeah. Gotcha, gotcha. So this is how I told the team to, like, now I think we’ve…
184 00:21:37.870 ⇒ 00:21:57.650 Uttam Kumaran: we’re doing a good job with Andy on the front end. I think now our job is to… we don’t expect… we’re not driving towards the fact that no errors will occur. What I want us to go after is the fact that the time between understanding the issue and figuring out which error code it is, and therefore figuring out what needs to happen.
185 00:21:57.780 ⇒ 00:21:59.120 Uttam Kumaran: We gotta shrink that.
186 00:21:59.120 ⇒ 00:21:59.550 Samuel Roberts: Yeah.
187 00:21:59.550 ⇒ 00:21:59.970 Uttam Kumaran: You know.
188 00:21:59.970 ⇒ 00:22:01.290 JanieceGarcia: Okay. Okay.
189 00:22:01.390 ⇒ 00:22:04.879 JanieceGarcia: Got it. Perfect. Thank you. You answered all my questions.
190 00:22:04.880 ⇒ 00:22:05.530 Uttam Kumaran: Okay.
191 00:22:06.180 ⇒ 00:22:10.329 Uttam Kumaran: Sorry, Yvette, just to, like, catch you up, we’re…
192 00:22:10.620 ⇒ 00:22:25.930 Uttam Kumaran: we’re… we built a little bit of a better system to, like, understand errors. I’ve sent this sort of spreadsheet in our email, but we’re doing a lot better job of tagging the negative feedback, with
193 00:22:25.980 ⇒ 00:22:39.229 Uttam Kumaran: the reason for it, and then, based on the reason, that’s either up to the Brainforce team to mediate, or it’s up to the ABC team. I think currently, we’re just getting thumbs down, and we don’t… it’s not clear, like.
194 00:22:39.260 ⇒ 00:22:46.399 Uttam Kumaran: who’s on the hook for it, so we’re gonna make that really clear. Second, we’re gonna actually improve the process of
195 00:22:46.480 ⇒ 00:23:03.139 Uttam Kumaran: figuring out what issue it is by using also AI there. So, like, we went through an example where, you know, someone asked a question, there wasn’t a record in the database, and they still gave, like, a thumbs down, or they said, hey, the estimator is Mike.
196 00:23:03.410 ⇒ 00:23:21.159 Uttam Kumaran: And, okay, that’s something where the AI should clearly look at the feedback, look at the question, and be like, okay, that’s… that’s clearly, like, there’s just no assignments, right? And so the ABC team should own that. So it’s now smart enough to take the feedback, the question.
197 00:23:21.510 ⇒ 00:23:25.410 Uttam Kumaran: to be like, yeah, it’s probably this, or it’s like, actually, I can’t figure out what the…
198 00:23:25.600 ⇒ 00:23:27.219 Uttam Kumaran: The potential issue is.
199 00:23:27.240 ⇒ 00:23:28.320 YvetteRuiz: Gotcha.
200 00:23:29.270 ⇒ 00:23:32.099 Uttam Kumaran: So, again, that should slim down.
201 00:23:32.630 ⇒ 00:23:39.370 Uttam Kumaran: You know, even looking at the list we currently went, at least, like, maybe 50-50 should give… make it clear, like, what team.
202 00:23:39.790 ⇒ 00:23:40.920 YvetteRuiz: Who owns it?
203 00:23:40.920 ⇒ 00:23:41.590 Uttam Kumaran: Owns what?
204 00:23:42.520 ⇒ 00:23:46.469 Uttam Kumaran: And then we’re gonna continue to get smarter on the ones that…
205 00:23:46.710 ⇒ 00:23:52.700 Uttam Kumaran: for some of these error codes, are there things that we can try to do automatically? Perfect. To change that, you know?
206 00:23:52.700 ⇒ 00:23:53.300 YvetteRuiz: Okay.
207 00:23:54.080 ⇒ 00:23:57.530 Uttam Kumaran: Cause some people are good with… some people are good with giving feedback. They’re like.
208 00:23:57.670 ⇒ 00:24:00.910 Uttam Kumaran: This person is the, like.
209 00:24:01.320 ⇒ 00:24:17.099 Uttam Kumaran: Travis is commercial, Victor is the level 8 termite tech. Okay, well, my question to Sam is, like, it’s… I feel like you can… we can build the thing that just updates the thing immediately, but probably what we should do is give Janice an ability to, like, press thumbs up.
210 00:24:17.800 ⇒ 00:24:19.779 Uttam Kumaran: on that. Like, AI should say.
211 00:24:19.970 ⇒ 00:24:27.759 Uttam Kumaran: hey, I can update the database to Mark Travis as commercial, do you want me to do that? Yes? Okay. Right? So shrinking that…
212 00:24:27.870 ⇒ 00:24:32.369 Uttam Kumaran: That time to get negative feedback, and for the change to come in, you know?
213 00:24:32.370 ⇒ 00:24:33.000 YvetteRuiz: Yeah.
214 00:24:33.000 ⇒ 00:24:33.580 Samuel Roberts: Nope.
215 00:24:35.170 ⇒ 00:24:36.989 JanieceGarcia: And that was cut… And this is… yeah.
216 00:24:36.990 ⇒ 00:24:37.570 Uttam Kumaran: Yeah, sorry.
217 00:24:37.570 ⇒ 00:24:40.939 JanieceGarcia: And that would cut down, like, right now, with what
218 00:24:41.360 ⇒ 00:24:46.370 JanieceGarcia: we would have to do is delete Travis, Out.
219 00:24:47.370 ⇒ 00:24:49.509 JanieceGarcia: And then go in and update.
220 00:24:50.000 ⇒ 00:24:55.750 JanieceGarcia: His zip codes, so then we can add him as commercial tech, so it’s a two-part.
221 00:24:56.040 ⇒ 00:24:56.660 Uttam Kumaran: Yes.
222 00:24:56.660 ⇒ 00:24:57.220 JanieceGarcia: Update.
223 00:24:57.220 ⇒ 00:25:01.850 Samuel Roberts: Something I wanted to discuss a little bit about the ergonomics of the forms as they are anyway, and how that kind of…
224 00:25:01.940 ⇒ 00:25:19.919 Samuel Roberts: fits into your workflow, Denise, and if there’s a way to make that an easier time in general, just updating, because I know the forms were a little clunky, and that was the way we were building it on NADM, but as we’re starting to migrate things anyway, we can maybe make it an easier, like, visual thing that you can
225 00:25:20.140 ⇒ 00:25:36.509 Samuel Roberts: change things rather than have to fill out a form that then does the changes. So that’s something I was, thinking about a little bit and discussing with the team, and, there’s some ideas there, but I wanted to get with you at some point to actually, like, understand your workflow and see how we can fit into that best, but… Absolutely.
226 00:25:37.080 ⇒ 00:25:50.360 YvetteRuiz: But the… what you’re saying right there, Sam and Utam, is when… once that’s put in there, then Janice is the person, or whoever the point person is, will be the one doing the updating, right? Is that what I’m understanding?
227 00:25:50.360 ⇒ 00:26:01.140 Uttam Kumaran: Yeah, I guess what we’re debating is what does updating look like? Yeah. Is it just, like, AI says, I can go update the database, do you want me to do that? Yes. Versus having to fill out…
228 00:26:01.140 ⇒ 00:26:18.699 Samuel Roberts: I think it’s gonna be a combination of, like, there’s certain updates you know ahead of time, and then certain times that someone asks something that hasn’t been updated yet, and then the AI can maybe say, hey, someone suggested this, is this a real update to make? And then it does it. So there should be, you know, it should still be a human in the loop both ways. One time it’s…
229 00:26:18.700 ⇒ 00:26:22.159 Samuel Roberts: human doing it, and the other time it’s approving the AI to do it.
230 00:26:22.860 ⇒ 00:26:24.230 YvetteRuiz: Okay, alright.
231 00:26:24.560 ⇒ 00:26:27.449 YvetteRuiz: Just wanted to make sure that I was understanding that.
232 00:26:27.560 ⇒ 00:26:28.390 YvetteRuiz: Okay.
233 00:26:29.040 ⇒ 00:26:43.869 YvetteRuiz: No, I mean, I think, Utam, again, thank you so much for jumping on that and just sending the recaps. I didn’t get to go back through your last night’s, email, thoroughly, but, I mean, yeah, everything that I’ve been seeing, everything is making sense, and…
234 00:26:43.870 ⇒ 00:26:46.029 Uttam Kumaran: It’s getting better,
235 00:26:46.900 ⇒ 00:27:02.710 Uttam Kumaran: I think part of the… the bigger picture thing here is we’re moving a lot of this, one, to ABC infrastructure, kind of working with Tim, and we’re upgrading a lot of pieces. But when we built this originally.
236 00:27:02.730 ⇒ 00:27:13.520 Uttam Kumaran: it was definitely just with, like, one scope in mind, and now we’ve added a lot of stuff, so there are some upgrades. We were originally gonna do a lot of that, you know, over the next 8 weeks, and I basically was like.
237 00:27:14.200 ⇒ 00:27:34.200 Uttam Kumaran: do as much as possible faster, because some of these are… making some of these changes are blocked by the way we developed the first system. So, the team has been good at and receptive for that, and so we are pushing a couple pieces, and one thing that I’ll share is sort of, like, what the larger timeline is, but basically all you’re going to see is the fact that we can
238 00:27:34.980 ⇒ 00:27:42.050 Uttam Kumaran: fix stuff like this way faster, and overall response times should go down.
239 00:27:42.240 ⇒ 00:27:46.580 Uttam Kumaran: Especially as we’re gonna start to have more and more, like, people use the system, so…
240 00:27:46.820 ⇒ 00:27:47.990 YvetteRuiz: Yeah, yeah, for sure.
241 00:27:47.990 ⇒ 00:27:48.650 Uttam Kumaran: Yeah.
242 00:27:48.650 ⇒ 00:28:02.960 YvetteRuiz: Well, I mean, I know already on our end, I know activity has… I mean, usage has picked up, and I know that there’s a lot more involvement today, and there’s going to continue to be more, so I’m excited that we’re moving, in that direction, for sure.
243 00:28:03.360 ⇒ 00:28:03.960 Uttam Kumaran: Great.
244 00:28:05.840 ⇒ 00:28:08.169 Uttam Kumaran: Let me go back to…
245 00:28:08.830 ⇒ 00:28:18.399 Uttam Kumaran: Here. So, yeah, another thing, and Amber’s out today, but she… she’s the one that worked on this deck, but she wanted me to cover that. She worked on…
246 00:28:18.460 ⇒ 00:28:28.019 Uttam Kumaran: Getting all the, you know, cancellations consolidated. So she’s gonna be working on a template for department managers to fill in.
247 00:28:28.060 ⇒ 00:28:42.559 Uttam Kumaran: You know, another thing that I think we’re going to continue to look at as part of the migration is just, we’re looking at the overall system by which we develop. So, is the way we’re doing the central dock still right? Are there pieces of that that we need to remove?
248 00:28:42.590 ⇒ 00:28:50.779 Uttam Kumaran: Or, like, consolidate, so I know that’s… that’s something that the team is also considering, but I know she’s making good progress here.
249 00:28:51.050 ⇒ 00:29:08.649 YvetteRuiz: Thank you for that. She, she was super helpful joining our meeting, which, Matt, I didn’t get to cover this part of it, just because we’re still… we have a lot of… some moving pieces on it, but we met on Tuesday, we brought Amber in, and… and I’ll… and like I said, I’ll talk more with you, but we really dove in to kind of…
250 00:29:09.020 ⇒ 00:29:24.470 YvetteRuiz: what are the true reasons when customers call in that are asking their cancellations? And then looking at the cancellations, what’s the top reasons? But we have a lot of cancellation reasons, and if we can really condense those reasons down to the actual
251 00:29:24.670 ⇒ 00:29:43.329 YvetteRuiz: what they’re really canceling. I feel like maybe we can clean Evolve up some, too, so then that way we can really stick to the core and really get better data off of that. But this is… this screenshot right here that they have pulled up right here, those were, like, the real reasons that our customers are truly canceling, that they’re giving us the reason.
252 00:29:43.330 ⇒ 00:29:50.809 MattBurns: Yeah, I agree, because I think if you have too many choices, it’s overwhelming, and, you know, but if you can narrow it down, because you’re right.
253 00:29:51.090 ⇒ 00:29:58.170 MattBurns: We shouldn’t… if it’s too expensive of a financial hardship, well, that’s kind of the same thing, so let’s have one answer there, as opposed to, you know…
254 00:29:58.440 ⇒ 00:30:02.890 MattBurns: four different… Choices are virtually the same thing, yeah.
255 00:30:02.890 ⇒ 00:30:06.929 YvetteRuiz: Yeah, and then building probing questions. So, like, okay, if it is…
256 00:30:07.160 ⇒ 00:30:14.720 YvetteRuiz: moving, okay, what are the ones going to, what’s… what are we going to start asking them? So, I’m excited about really drilling down and getting that
257 00:30:14.840 ⇒ 00:30:16.220 YvetteRuiz: Cleaned up some.
258 00:30:17.490 ⇒ 00:30:20.350 Uttam Kumaran: Okay Okay, perfect.
259 00:30:22.380 ⇒ 00:30:24.699 Uttam Kumaran: And then… let me just…
260 00:30:30.060 ⇒ 00:30:42.019 Uttam Kumaran: Yeah, so we are, our team is also working on, sort of, this migration effort. We kind of built out, you know, a Gantt chart of items. We sort of have purview into the next few weeks, but sort of…
261 00:30:42.020 ⇒ 00:30:59.030 Uttam Kumaran: starting to build out, you know, the rest of it. I think this is, Sam, maybe something that, you know, in future meetings, I can have you just give a little bit of an update. The lovely thing is we’re working kind of closer with Tim. A lot of this is going to move onto, like, ABC infrastructure, and again, like.
262 00:30:59.030 ⇒ 00:31:13.800 Uttam Kumaran: ultimately, for the user, it’s that things are faster, and that they’re more accurate. And so, we should be able to see… I mean, I’m… I’m always guiding the team to try to attack the execution time problem, and this is really, like.
263 00:31:14.290 ⇒ 00:31:32.539 Uttam Kumaran: this has to happen for us to start to move towards sub-10 seconds and then sub-5 seconds, you know, on average. And sort of on that note, I did want to share, like, some things we’re doing on the actual, like, measurement side. And so this is something that, you know, I worked a little bit on this week.
264 00:31:32.590 ⇒ 00:31:34.769 Uttam Kumaran: Which is looking at,
265 00:31:35.120 ⇒ 00:31:54.569 Uttam Kumaran: just, like, our, like, quality performance, starting to build a little bit more of a dashboard that shows, you know, insights. And so one of the things that I wanted to start to see is, like, what is our thumbs-up rate? What is our average execution time? And so this dashboard is a… is, like, kind of what we’re gonna start to…
266 00:31:54.620 ⇒ 00:31:58.369 Uttam Kumaran: To guide towards, to use, to start to look at,
267 00:31:59.200 ⇒ 00:32:15.149 Uttam Kumaran: to look at how average response time is tracking, how much… how many things need escalations. And then the other big piece, you know, for our internal team is looking at the execution times. And so, this is where in… in…
268 00:32:15.170 ⇒ 00:32:26.669 Uttam Kumaran: engineering, you have these things called P80, P90, P95. These are just, like, the percentiles. So, this means that 80% of responses are faster than, you know, 12 seconds.
269 00:32:26.750 ⇒ 00:32:35.819 Uttam Kumaran: 90% are faster than around 14, and then 95 are faster than around 15. And so our job is to see these
270 00:32:35.980 ⇒ 00:32:39.310 Uttam Kumaran: all kind of collapse over time.
271 00:32:39.610 ⇒ 00:32:44.220 Uttam Kumaran: Ultimately, there are gonna be some queries that drive the average up.
272 00:32:44.380 ⇒ 00:32:50.319 Uttam Kumaran: So that’s why we want to see, on a percentile basis, are most of them happening
273 00:32:50.480 ⇒ 00:32:56.060 Uttam Kumaran: less than 15 seconds, and then for me, I’m like, okay, now we need to move P90 down to 10.
274 00:32:56.140 ⇒ 00:33:13.739 Uttam Kumaran: And so that’s, like, how we’re gonna sort of drive this. And then the other thing on the bottom here is to start to look at, like, thumbs-up counts overall, and also look at the number exchanges and performance metrics by team. So, for our side, I want to start to look at, are certain teams
275 00:33:13.940 ⇒ 00:33:31.370 Uttam Kumaran: thumbs downing more, or thumbs up upping more, right? And so that’s the segments. So when… when we start to bring in each of these, like, service leaders, their job is to look at their segment and start to look at, hey, like, I want to look at all the questions that are coming for lawn and tree.
276 00:33:31.760 ⇒ 00:33:45.349 Uttam Kumaran: and be able to look at, okay, what’s… like, what are the average execution times, and what are the number of exchanges, and what are the thumbs up and thumbs down rates? And then the last piece I have here is
277 00:33:45.570 ⇒ 00:33:54.400 Uttam Kumaran: I just was like, hey, I just want to see a list of all the thumbs down, and I want us to be able to clearly see that in one place, where you can see
278 00:33:54.560 ⇒ 00:34:12.840 Uttam Kumaran: what the ask was, and, like, what the actual, like, response was. And so this was a, you know, this was a thumbs down, and I’m… maybe we’ll work a little bit on getting this to expand, but you can just start to see that in one place, and so it’s easy for people to come in and see, like.
279 00:34:12.889 ⇒ 00:34:15.339 Uttam Kumaran: Just a list of responses, you know?
280 00:34:15.440 ⇒ 00:34:22.730 Uttam Kumaran: And so that’s really helpful, and then we’re also gonna work on a few more views, which is, like, identifying
281 00:34:23.040 ⇒ 00:34:33.799 Uttam Kumaran: you know, have error patterns, changed across different categories, so some of these are, like, work in progress. But now that we have those emails.
282 00:34:33.800 ⇒ 00:34:36.540 YvetteRuiz: It’s very easy for us to…
283 00:34:36.570 ⇒ 00:34:38.110 Uttam Kumaran: Start to divide.
284 00:34:38.239 ⇒ 00:34:53.559 Uttam Kumaran: you know, the questions up and start to segment things. And so, again, this is, like, I think very, very similar to the work we’re doing on the discovery side, which is just starting to segment by the service lines, by who’s asking, by the types of questions.
285 00:34:53.560 ⇒ 00:35:02.120 Uttam Kumaran: thumbs up versus thumbs down, and then it gives you a clear direction on where the bottlenecks are. So, I think we’re gonna… we’ll end up moving
286 00:35:02.370 ⇒ 00:35:17.460 Uttam Kumaran: to some version of this as our, like, primary dashboard. That way, it’s really clear, are there any… are there anything that are, like, taking 20 or 30 seconds that our team needs to look at immediately? Are there pieces that,
287 00:35:17.780 ⇒ 00:35:25.660 Uttam Kumaran: you know, and then roughly, like, are we seeing adoption across, like, the core teams? And then our other dashboard is still, you know,
288 00:35:26.560 ⇒ 00:35:30.379 Uttam Kumaran: Still exists, where you can go in and start to see,
289 00:35:30.820 ⇒ 00:35:35.140 Uttam Kumaran: Literally individual chats all in one place, so…
290 00:35:35.430 ⇒ 00:35:40.039 Uttam Kumaran: Just getting better at the observability, piece.
291 00:35:40.180 ⇒ 00:35:43.989 Uttam Kumaran: And then I think, again, my directive for Sam and the AI team is to
292 00:35:44.180 ⇒ 00:35:48.270 Uttam Kumaran: Help us improve the speed at which we fix all the thumbs down.
293 00:35:48.410 ⇒ 00:35:56.410 Uttam Kumaran: You know? And some of the thumbs down, we may not… we’re not going to get to zero, but as a percentage of questions, we want to see that go down.
294 00:35:57.310 ⇒ 00:36:13.449 YvetteRuiz: Yeah, no, for sure. I really like that other dashboard, Utam. I think that has a lot of value. I mean, ever since we broke down the teams, that’s been really helpful the past couple weeks, kind of seeing the usage, it’s helped the team. But even to your point, to where, what are the questions that are being asked?
295 00:36:13.450 ⇒ 00:36:23.660 YvetteRuiz: That’s been very helpful. Amber had created me a report that it sent to me, and I kind of just sort it just to kind of look at it, but if you put it through here in the dashboard.
296 00:36:23.660 ⇒ 00:36:24.230 Uttam Kumaran: Yeah.
297 00:36:24.230 ⇒ 00:36:35.240 YvetteRuiz: I mean, it’s… and the leaders can come in here and start using it and be, you know, be able to read that. That’s going to bring a lot of value, because that’s going to tell us a story on areas that we need to work on, as you pointed out.
298 00:36:35.580 ⇒ 00:36:36.849 Uttam Kumaran: Okay, okay, perfect.
299 00:36:39.020 ⇒ 00:36:55.820 Uttam Kumaran: Yeah, this is just something we tried to push part of this out this week, just so I can start to get better visibility. But I think everything we’re gonna start to break out and have a really clear area to see anything where the response times are high, or the feedback is low.
300 00:36:55.830 ⇒ 00:36:59.899 Uttam Kumaran: That’s, like, what we’re gonna con… that’s where we want the attention to go, you know?
301 00:37:02.440 ⇒ 00:37:03.780 YvetteRuiz: Perfect.
302 00:37:03.780 ⇒ 00:37:16.769 Uttam Kumaran: Yeah, I think that’s, like, most of what we, you know, wanted to cover today. I think on the other workstream, Matt, me, Amber, Steven, and Bo met yesterday and spoke through a bunch of items.
303 00:37:16.770 ⇒ 00:37:26.599 Uttam Kumaran: So we have a pretty clear plan. I think our… our goal now, coming out of, the holiday break, is, like, we’ll probably end up coming into the office and doing
304 00:37:26.600 ⇒ 00:37:41.389 Uttam Kumaran: like, a little bit of, like, a preliminary, slide deck on, like, a lot of the things that we found, and getting feedback from Bo and Steven. And then we’re still, I think, driving well towards, like, our mid-January timeline. It’s been awesome.
305 00:37:41.400 ⇒ 00:37:46.140 Uttam Kumaran: I told him it’s been great meeting everybody. Like, everybody’s so motivated.
306 00:37:46.200 ⇒ 00:37:48.790 Uttam Kumaran: We have not met up someone who is, like.
307 00:37:48.980 ⇒ 00:37:59.850 Uttam Kumaran: this place sucks, and it’s going to hell, you know? It’s like, I’m not gonna… I don’t wanna help it out. You know, you’d be surprised, we go to a lot of companies, and they’re like, is this on the record? Like, you know, and so…
308 00:37:59.850 ⇒ 00:38:00.710 MattBurns: It’s the humble thing.
309 00:38:00.710 ⇒ 00:38:12.110 Uttam Kumaran: has been really, really passionate about finding a way to grow the company, and… but… and everybody is very, very busy, and so for me, when I talk to folks like
310 00:38:12.110 ⇒ 00:38:25.280 Uttam Kumaran: David, who I’m like, okay, this is our perfect internal champion on the data side, who I know exactly a bunch of ways where I can help him scale his effectiveness. I talked to Julie about all of the great work on the Evolve side.
311 00:38:25.280 ⇒ 00:38:40.939 Uttam Kumaran: And I could tell that until we have… until you’re able to put a lot of reporting on top of it, the team… the whole company’s not going to know a lot of the work that goes into cleaning that system up, having clear cancellation codes, referral codes. We learned a lot about rewards.
312 00:38:40.940 ⇒ 00:38:54.689 Uttam Kumaran: learning a lot about commercial side, so there’s just a ton of opportunity that we’re gonna share. So, got a big two-week, 3-week crash course into everything, but I feel really, really positive about, like, the outcomes that we’re gonna share.
313 00:38:54.940 ⇒ 00:39:00.989 MattBurns: Well, and I, you know, I’m glad to hear that. I know that most, all of our people feel like
314 00:39:01.780 ⇒ 00:39:04.190 MattBurns: We’ve got the best service staff we’ve had.
315 00:39:04.690 ⇒ 00:39:07.809 MattBurns: I think we have the best CSR staff we’ve had.
316 00:39:08.160 ⇒ 00:39:12.140 MattBurns: You know, we… We’re doing a lot of things right, yet…
317 00:39:12.970 ⇒ 00:39:15.919 MattBurns: What’s going on with our lead flow and conversions, and, you know, how
318 00:39:16.240 ⇒ 00:39:18.530 MattBurns: How can we maximize it? Because we got…
319 00:39:18.700 ⇒ 00:39:26.940 MattBurns: I think we got a lot of things in place to capitalize on You know, just… just…
320 00:39:27.110 ⇒ 00:39:37.980 MattBurns: more opportunities. If we could capitalize on the more… get more opportunities, I think we can… I think we can make it, a good experience for all our customers, and, you know, that’s what we want, so…
321 00:39:38.300 ⇒ 00:39:41.810 Uttam Kumaran: Yeah, I agree. And I think a lot… I think it’s also just…
322 00:39:41.880 ⇒ 00:39:58.839 Uttam Kumaran: through a lot of automation and some data, you’re gonna find that you want to turn people like David, who, from spending 80% of his time, you know, preparing data, and then spending 20%, like, actually thinking about the changes, like, you want to flip that.
323 00:39:58.840 ⇒ 00:40:01.130 MattBurns: Right? You want him to be able to…
324 00:40:01.130 ⇒ 00:40:12.830 Uttam Kumaran: start to take the insights that he’s developed for Yvette and go org by org by org to, like, get them data-driven, right? And… but I know, I talked to him, and he was telling me about, like, his goals. I’m like.
325 00:40:12.910 ⇒ 00:40:31.999 Uttam Kumaran: how are you gonna do that? Like, it’s just you and Brian, and you’re doing all this, like, there’s… and I can know there’s no way he’s gonna be able to scale that. And I told him the answer, you know, I just told him that I don’t think the answer is, oh, I need a team of, like, 50 people. The answer is you have to do some automation, you have to think about some analytics best practices.
326 00:40:32.000 ⇒ 00:40:34.040 Uttam Kumaran: Thinking about, like, how do you actually
327 00:40:34.040 ⇒ 00:40:41.299 Uttam Kumaran: bring data to the right parts of the organization. And so it’s going to be really, really, you know, positive, so…
328 00:40:42.040 ⇒ 00:40:43.009 MattBurns: Good deal. Yeah.
329 00:40:43.580 ⇒ 00:40:45.820 MattBurns: Well, good. Well, thank you guys, and .
330 00:40:46.050 ⇒ 00:40:46.910 YvetteRuiz: Deep.
331 00:40:47.080 ⇒ 00:40:49.249 MattBurns: So far, so good, that sounds great, so…
332 00:40:50.220 ⇒ 00:40:53.069 YvetteRuiz: Thank you so much again, Utam, for everything.
333 00:40:53.070 ⇒ 00:40:54.740 Uttam Kumaran: Yeah, of course, please email me if…
334 00:40:55.530 ⇒ 00:41:05.309 Uttam Kumaran: Yeah, please email me if there’s anything else, or anything else I can help with. We’ll follow up today with another email of some things that, that we’ve done today, so I appreciate the time.
335 00:41:05.310 ⇒ 00:41:09.799 YvetteRuiz: And we really appreciate Amber, too, and thank you for the gift, Uten, that was super kind.
336 00:41:09.800 ⇒ 00:41:11.479 Uttam Kumaran: Oh, yeah, of course, of course.
337 00:41:11.580 ⇒ 00:41:13.519 MattBurns: Thank you guys, appreciate it.
338 00:41:13.520 ⇒ 00:41:14.260 Uttam Kumaran: Alright. Yeah.
339 00:41:14.550 ⇒ 00:41:16.139 Uttam Kumaran: Okay, thanks everyone. Bye guys.
340 00:41:16.470 ⇒ 00:41:17.130 Samuel Roberts: Oh.